Yubo Song, Rui Kong
The rapid growth of artificial intelligence (AI) computing is transforming data centers into large, dynamic electrical loads, primarily constrained by energy availability and grid-connection capacity. This article explores AI data centers as grid-interactive computing systems, reviewing grid-integration bottlenecks and evolving connection policies that foster new technological trends through spatio-temporal flexibility. It maps the evolution of power-delivery architectures from medium-voltage interfaces to chip-level while discussing advanced technologies like higher-voltage DC distribution, solid-state transformers, and liquid cooling. A three-level stability framework is established, spanning rack-level DC-bus dynamics, facility-level converter interactions, and system-level grid-coupled behavior, connecting dominant instability mechanisms such as constant power load effects and impedance interactions with modeling and mitigation strategies. The findings highlight grid-to-chip co-design as a critical requirement for scalable AI infrastructure, integrating computing workloads, power-delivery systems, energy buffers, and grid operation.
@article{c6dc740b-c027-4886-992f-d3e3433277b5,
title={2026 Song Grid to Chip AI Data Centers},
author={Yubo Song and Rui Kong},
year={2026},
language={en}
}TY - JOUR TI - 2026 Song Grid to Chip AI Data Centers AU - Yubo Song AU - Rui Kong PY - 2026 LA - en ER -
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